{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pytorch 支持 sklearn 的重参数搜索需要其他的库。一般的项目中设置好命令行脚本后，可以通过更改脚本参数手动实现超参数搜索"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-17T06:04:08.511766Z",
     "start_time": "2025-01-17T06:03:59.306541Z"
    }
   },
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "    \n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=12, micro=3, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cpu\n",
      "cpu\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-17T06:04:24.281781Z",
     "start_time": "2025-01-17T06:04:24.045793Z"
    }
   },
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "print(housing.DESCR)\n",
    "print(housing.data.shape)\n",
    "print(housing.target.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _california_housing_dataset:\n",
      "\n",
      "California Housing dataset\n",
      "--------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      ":Number of Instances: 20640\n",
      "\n",
      ":Number of Attributes: 8 numeric, predictive attributes and the target\n",
      "\n",
      ":Attribute Information:\n",
      "    - MedInc        median income in block group\n",
      "    - HouseAge      median house age in block group\n",
      "    - AveRooms      average number of rooms per household\n",
      "    - AveBedrms     average number of bedrooms per household\n",
      "    - Population    block group population\n",
      "    - AveOccup      average number of household members\n",
      "    - Latitude      block group latitude\n",
      "    - Longitude     block group longitude\n",
      "\n",
      ":Missing Attribute Values: None\n",
      "\n",
      "This dataset was obtained from the StatLib repository.\n",
      "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\n",
      "\n",
      "The target variable is the median house value for California districts,\n",
      "expressed in hundreds of thousands of dollars ($100,000).\n",
      "\n",
      "This dataset was derived from the 1990 U.S. census, using one row per census\n",
      "block group. A block group is the smallest geographical unit for which the U.S.\n",
      "Census Bureau publishes sample data (a block group typically has a population\n",
      "of 600 to 3,000 people).\n",
      "\n",
      "A household is a group of people residing within a home. Since the average\n",
      "number of rooms and bedrooms in this dataset are provided per household, these\n",
      "columns may take surprisingly large values for block groups with few households\n",
      "and many empty houses, such as vacation resorts.\n",
      "\n",
      "It can be downloaded/loaded using the\n",
      ":func:`sklearn.datasets.fetch_california_housing` function.\n",
      "\n",
      ".. rubric:: References\n",
      "\n",
      "- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
      "  Statistics and Probability Letters, 33 (1997) 291-297\n",
      "\n",
      "(20640, 8)\n",
      "(20640,)\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-19T03:20:55.356503800Z",
     "start_time": "2024-07-19T03:20:55.349888300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([[ 8.32520000e+00,  4.10000000e+01,  6.98412698e+00,\n",
      "         1.02380952e+00,  3.22000000e+02,  2.55555556e+00,\n",
      "         3.78800000e+01, -1.22230000e+02],\n",
      "       [ 8.30140000e+00,  2.10000000e+01,  6.23813708e+00,\n",
      "         9.71880492e-01,  2.40100000e+03,  2.10984183e+00,\n",
      "         3.78600000e+01, -1.22220000e+02],\n",
      "       [ 7.25740000e+00,  5.20000000e+01,  8.28813559e+00,\n",
      "         1.07344633e+00,  4.96000000e+02,  2.80225989e+00,\n",
      "         3.78500000e+01, -1.22240000e+02],\n",
      "       [ 5.64310000e+00,  5.20000000e+01,  5.81735160e+00,\n",
      "         1.07305936e+00,  5.58000000e+02,  2.54794521e+00,\n",
      "         3.78500000e+01, -1.22250000e+02],\n",
      "       [ 3.84620000e+00,  5.20000000e+01,  6.28185328e+00,\n",
      "         1.08108108e+00,  5.65000000e+02,  2.18146718e+00,\n",
      "         3.78500000e+01, -1.22250000e+02]])\n",
      "--------------------------------------------------\n",
      "array([4.526, 3.585, 3.521, 3.413, 3.422])\n"
     ]
    }
   ],
   "source": [
    "# print(housing.data[0:5])\n",
    "import pprint  #打印的格式比较 好看\n",
    "\n",
    "pprint.pprint(housing.data[0:5])\n",
    "print('-'*50)\n",
    "pprint.pprint(housing.target[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-17T06:04:38.792821Z",
     "start_time": "2025-01-17T06:04:38.590651Z"
    }
   },
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#拆分训练集和测试集，random_state是随机种子,同样的随机数种子，是为了得到同样的随机值\n",
    "x_train_all, x_test, y_train_all, y_test = train_test_split(\n",
    "    housing.data, housing.target, random_state = 7)\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(\n",
    "    x_train_all, y_train_all, random_state = 11)\n",
    "# 训练集\n",
    "print(x_train.shape, y_train.shape)\n",
    "# 验证集\n",
    "print(x_valid.shape, y_valid.shape)\n",
    "# 测试集\n",
    "print(x_test.shape, y_test.shape)\n",
    "\n",
    "dataset_maps = {\n",
    "    \"train\": [x_train, y_train],\n",
    "    \"valid\": [x_valid, y_valid],\n",
    "    \"test\": [x_test, y_test],\n",
    "}\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11610, 8) (11610,)\n",
      "(3870, 8) (3870,)\n",
      "(5160, 8) (5160,)\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-17T06:04:53.931823Z",
     "start_time": "2025-01-17T06:04:53.925971Z"
    }
   },
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(x_train)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ],
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建数据集"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-17T06:05:14.993923Z",
     "start_time": "2025-01-17T06:05:14.988051Z"
    }
   },
   "source": [
    "from torch.utils.data import Dataset\n",
    "\n",
    "class HousingDataset(Dataset):\n",
    "    def __init__(self, mode='train'):\n",
    "        self.x, self.y = dataset_maps[mode]\n",
    "        self.x = torch.from_numpy(scaler.transform(self.x)).float()\n",
    "        self.y = torch.from_numpy(self.y).float().reshape(-1, 1)\n",
    "            \n",
    "    def __len__(self):\n",
    "        return len(self.x)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        return self.x[idx], self.y[idx]\n",
    "    \n",
    "    \n",
    "train_ds = HousingDataset(\"train\")\n",
    "valid_ds = HousingDataset(\"valid\")\n",
    "test_ds = HousingDataset(\"test\")"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-25T03:30:34.945253200Z",
     "start_time": "2024-04-25T03:30:34.894354300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(tensor([-0.2981,  0.3523, -0.1092, -0.2506, -0.0341, -0.0060,  1.0806, -1.0611]),\n tensor([1.5140]))"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ds[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-17T06:05:47.746736Z",
     "start_time": "2025-01-17T06:05:47.743621Z"
    }
   },
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "batch_size = 256 #大一些，可以加快训练速度\n",
    "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(valid_ds, batch_size=batch_size, shuffle=False)\n",
    "test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False)"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-17T06:06:03.640817Z",
     "start_time": "2025-01-17T06:06:03.637447Z"
    }
   },
   "source": [
    "#回归模型我们只需要1个数\n",
    "\n",
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self, input_dim=8):\n",
    "        super().__init__()\n",
    "        self.linear_relu_stack = nn.Sequential(\n",
    "            nn.Linear(input_dim, 30),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(30, 1)\n",
    "            )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # x.shape [batch size, 8]\n",
    "        logits = self.linear_relu_stack(x)\n",
    "        # logits.shape [batch size, 1]\n",
    "        return logits"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-17T06:08:55.328460Z",
     "start_time": "2025-01-17T06:08:55.325289Z"
    }
   },
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            patience (int, optional): Number of epochs with no improvement after which training will be stopped.. Defaults to 5.\n",
    "            min_delta (float, optional): Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute \n",
    "                change of less than min_delta, will count as no improvement. Defaults to 0.01.\n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.min_delta = min_delta\n",
    "        self.best_metric = -1\n",
    "        self.counter = 0\n",
    "        \n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:\n",
    "            # update best metric\n",
    "            self.best_metric = metric\n",
    "            # reset counter \n",
    "            self.counter = 0\n",
    "        else: \n",
    "            self.counter += 1\n",
    "            \n",
    "    @property\n",
    "    def early_stop(self):\n",
    "        return self.counter >= self.patience\n"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-17T06:08:57.212672Z",
     "start_time": "2025-01-17T06:08:57.209789Z"
    }
   },
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "@torch.no_grad()\n",
    "def evaluating(model, dataloader, loss_fct):\n",
    "    loss_list = []\n",
    "    for datas, labels in dataloader:\n",
    "        datas = datas.to(device)\n",
    "        labels = labels.to(device)\n",
    "        # 前向计算\n",
    "        logits = model(datas)\n",
    "        loss = loss_fct(logits, labels)         # 验证集损失\n",
    "        loss_list.append(loss.item())\n",
    "        \n",
    "    return np.mean(loss_list)\n"
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-17T06:09:08.508057Z",
     "start_time": "2025-01-17T06:09:08.503798Z"
    }
   },
   "source": [
    "# 训练\n",
    "def training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=None,\n",
    "    save_ckpt_callback=None,\n",
    "    early_stop_callback=None,\n",
    "    eval_step=500,\n",
    "    ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "    \n",
    "    global_step = 0\n",
    "    model.train()\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # training\n",
    "            for datas, labels in train_loader:\n",
    "                datas = datas.to(device)\n",
    "                labels = labels.to(device)\n",
    "                # 梯度清空\n",
    "                optimizer.zero_grad()\n",
    "                # 模型前向计算\n",
    "                logits = model(datas)\n",
    "                # 计算损失\n",
    "                loss = loss_fct(logits, labels)\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    " \n",
    "                loss = loss.cpu().item()\n",
    "                # record\n",
    "                \n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss, \"step\": global_step\n",
    "                })\n",
    "                \n",
    "                # evaluating\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss, \"step\": global_step\n",
    "                    })\n",
    "                    model.train()\n",
    "\n",
    "                    # 早停 Early Stop\n",
    "                    if early_stop_callback is not None:\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict\n",
    "                    \n",
    "                # udate step\n",
    "                global_step += 1\n",
    "                pbar.update(1)\n",
    "                pbar.set_postfix({\"epoch\": epoch_id})\n",
    "        \n",
    "    return record_dict\n"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-17T06:09:15.755957Z",
     "start_time": "2025-01-17T06:09:15.752945Z"
    }
   },
   "source": [
    "#画线要注意的是损失是不一定在零到1之间的\n",
    "def plot_learning_curves(record_dict, sample_step=5):\n",
    "    # build DataFrame\n",
    "    train_df = pd.DataFrame(record_dict[\"train\"]).set_index(\"step\").iloc[::sample_step]\n",
    "    val_df = pd.DataFrame(record_dict[\"val\"]).set_index(\"step\")\n",
    "\n",
    "    # plot\n",
    "    for idx, item in enumerate(train_df.columns):\n",
    "        plt.plot(train_df.index, train_df[item], label=f\"train_{item}\")\n",
    "        plt.plot(val_df.index, val_df[item], label=f\"val_{item}\")\n",
    "        plt.grid()\n",
    "        plt.legend()\n",
    "        # plt.xticks(range(0, train_df.index[-1], 10*sample_step), range(0, train_df.index[-1], 10*sample_step))\n",
    "        plt.xlabel(\"step\")\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "# plot_learning_curves(record)  #横坐标是 steps"
   ],
   "outputs": [],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "# 网格搜索，for循环实现"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-17T06:17:45.512951Z",
     "start_time": "2025-01-17T06:17:11.768175Z"
    }
   },
   "source": [
    "for lr in [1e-2, 3e-2, 3e-1, 1e-3]:\n",
    "\n",
    "    epoch = 100\n",
    "\n",
    "    model = NeuralNetwork()\n",
    "\n",
    "    # 1. 定义损失函数 采用MSE损失\n",
    "    loss_fct = nn.MSELoss()\n",
    "    # 2. 定义优化器 采用SGD\n",
    "    # Optimizers specified in the torch.optim package\n",
    "    optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9)\n",
    "\n",
    "    # 3. early stop\n",
    "    early_stop_callback = EarlyStopCallback(patience=10, min_delta=1e-3)\n",
    "\n",
    "    model = model.to(device)\n",
    "    record = training(\n",
    "        model, \n",
    "        train_loader, \n",
    "        val_loader, \n",
    "        epoch, \n",
    "        loss_fct, \n",
    "        optimizer, \n",
    "        early_stop_callback=early_stop_callback,\n",
    "        eval_step=len(train_loader)\n",
    "        )\n",
    "    print(\"lr: {}\".format(lr))\n",
    "    plot_learning_curves(record)\n",
    "    model.eval()\n",
    "    loss = evaluating(model, val_loader, loss_fct)\n",
    "    print(f\"loss:     {loss:.4f}\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/4600 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "1fd9aadc101044fbb5478a69963803d2"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 66 / global_step 3036\n",
      "lr: 0.01\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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zz/q15DXmz5+PXr16VdpRFaFqKwkSIyIizjpIMVdgIUWcDgYWRETBJgcluQXidWTqaplNkoFFxWwr03Rg2eFsVGYsiIiIQsc0gUW+xZl8YWBBREQUOqbLWFgcZ9/HR0RERJU8sGCNBRERUeiZJrCwuwILdoUQERFVkMBi7NixahiK8daqVSuEg3x3V0huqBeFiIio0irzcFO5DOzvv/9e+AIR4TFitUAv3nTYQ70oRERElVaZowIJJGrVqoVwrbGwssaCiIio4gQW27ZtQ506ddSEHD169MD48ePV9J/Fyc3NVTddRkaGe8awQMzS5t0VouXnBPR1zUZvG7ZR6dhW/mE7+Yft5B+2U/i2lb/vY9HKcEm6GTNm4NSpU2jZsiXS0tIwbtw47N+/H+vXry/2evRSlyHP8zZp0iQ1J3mgWNd8jqGO2fgjcRgymg4L2OsSERERkJ2djeHDhyM9PR2JiYmBCSy8nTx5Eg0bNsTrr7+O2267ze+MRf369XH06NESF6ysZv3nVgzJ/gmbm96Kpte/HLDXNRuJOGfNmoX+/fuH1RSw4Yht5R+2k3/YTv5hO4VvW8nxOyUlpdTA4qwqL6tWrYoWLVpg+/btxT4nOjpa3bxJIwSyIQoszku82rQCrox+CHT7mxnbyj9sJ/+wnfzDdgq/tvL3Pc5qHgvpFtmxYwdq166NUGPxJhERUeiVKbB45JFHMG/ePOzatQt//fUXhg0bpq6udsMNNyBsJshiYEFERBQyZeoK2bdvnwoijh07hho1auCCCy7A4sWL1c/hEljwWiFEREQVJLD45ptvEK70wMLGKb2JiIhCxnTXCmHGgoiIKHRMF1jYWGNBREQUMqYLLFi8SUREFDqmCSwKGFgQERGFnAkzFqyxICIiChUGFkRERBQwJgosnFN6syuEiIgodMwTWFg5KoSIiCjUTBNYFLjm+mLGgoiIKHRM2BViD/WiEBERVVqmCSwKXF0hzFgQERGFjmkCC868SUREFHqmCSwKrM6uEJuWD2haqBeHiIioUjJNYGGHM2OhFHAuCyIiolAwXVeIUpAbykUhIiKqtExXvKnYWWdBREQUCqYJLGCxIl+zOX9mxoKIiCgkTBNYWCwW5LsmyYKdgQUREVEomCewAJCnBxYs3iQiIgoJ0wQWIk8fGcKuECIiopAwTWBhsRgyFizeJCIiCgnzBBawIFdjxoKIiCiUTBNYSJGFu3izgBkLIiKiUDBn8Sa7QoiIiELCZDUW7AohIiIKJdMEFiJP4zwWREREoWSq4k3WWBAREYWWSbtCGFgQERGFgnkCCwC5LN4kIiIKKfMEFizeJCIiCjnTBBYSWbB4k4iIKLTME1iAE2QRERGFmskmyGLxJhERUSiZJ7DgRciIiIhCzjyBBSws3iQiIgoxc2UsWLxJREQUUqYJLIS7K6QgP9SLQkREVCmZtHiTGQsiIqJQME9gweJNIiKikDNNYCE5izyNGQsiIqJQMlXGwj1BFos3iYiIQsKcFyFj8SYREVFImCawECzeJCIiCi3TBBYs3iQiIgo98wQWLN4kIiIKOXMWb/IiZERERCFhnsDCOPMmu0KIiIhCwjSBhaQsclm8SUREFFLmCSyYsSAiIqrYgcVLL70Ei8WCBx98EOHQFZKvX92UGQsiIqKKFVgsW7YMH3zwATp06IDwGW7q6gpx2AGHI9SLREREVOmcUWBx6tQpjBgxAh999BGqVauGsCveFBwZQkREFHSGI7H/7r33XlxyySXo168fXnjhhRKfm5ubq266jIwMdZ+fn69ugaI5HIUZC3n9nCwAtoC9vlnobR7ItjcrtpV/2E7+YTv5h+0Uvm3l7/uUObD45ptvsHLlStUV4o/x48dj3LhxRR6fOXMm4uLiECi791g9Mha//zYdeZGJAXt9s5k1a1aoF6HCYFv5h+3kH7aTf9hO4ddW2dnZgQ8s9u7diwceeEB9iJiYGL/+Z8yYMXjooYc8Mhb169fHgAEDkJgYuAP/yl82AWl7UWCJgE2zo1+fC4HEugF7fbOQiFO+v/79+yMysjDDQ0WxrfzDdvIP28k/bKfwbSu9xyGggcWKFStw+PBhdO7c2f1YQUEB5s+fj7ffflt1edhsnt0P0dHR6uZNGiGQDRFhc5aL2C1RKrCItDjkTQL2+mYT6PY3M7aVf9hO/mE7+YftFH5t5e97lCmw6Nu3L9atW+fx2C233IJWrVph9OjRRYKKUCiwuD4457IgIiIKujIFFgkJCWjXrp3HY/Hx8ahevXqRx4NN5tMQdmskUMC5LIiIiELBNDNvOsMKZiyIiIgq3HBTo7lz5yKcuAMLzmNBREQUdObJWLhSFnZrlPMHdoUQEREFnXkCC1dnCLtCiIiIQsd8GQt3VwgzFkRERMFmnsDCdc+MBRERUeiYJrBAkYwFAwsiIqJgM09g4aLmsRDsCiEiIgo60wQWLN4kIiIKPfMEFizeJCIiCjnzBBaue9ZYEBERhY55MxbsCiEiIgo68wQWrpwFu0KIiIhCxzSBhc5ucU3pzYwFERFR0JknsHB1hRRYXNdVY8aCiIgo6ExXvJmvZyxYvElERBR05gksXNWb7owFu0KIiIiCzjyBhXeNBbtCiIiIgs40gYWOw02JiIhCx4TzWLB4k4iIKFTME1i47vOhd4Xkh3JxiIiIKiXzBBaulIU7Y2FnxoKIiCjYTBNYoMhwUwYWREREwWa6GgsONyUiIgod0wQWunxe3ZSIiChkzDcqBAwsiIiIQsU8gYVrXEi+HliweJOIiCjozJexsPKy6URERKFinsDCdV+YsWBXCBERUbCZcB4L1lgQERGFimkCC12+PtxUKwAcBaFeHCIiokrFfIGFPqW3YAEnERFRUJn3ImSCBZxERERBZZ7AwnVfoBkDC16IjIiIKJjME1joKQu5t0U7f2ZXCBERUVCZJrDQadCACFdgwZEhREREQWW6rhBNA2BzFXAyY0FERBRU5gksXJGFxBXuwILFm0REREFlnsDCda9JyiJCDyxYvElERBRMpgks9JSFM2PB4k0iIqJQMF3GQnFnLBhYEBERBZNpAgv4LN7kqBAiIqJgMl3xJoxdIRxuSkREFFTmCSxcnSGexZsMLIiIiILJpMNNWbxJREQUCuYJLIw1FizeJCIiCgmT1liweJOIiCgUTBNYeFwrhMWbREREIWGiwMLioyuEgQUREVEwmSawYPEmERFR6JknsNB/YPEmERFRxQgs3nvvPXTo0AGJiYnq1qNHD8yYMQPhlbGQGgsWbxIREYV9YFGvXj289NJLWLFiBZYvX46LL74Yl19+OTZs2IBwmSBLYfEmERFRSESU5clDhw71+P1f//qXymIsXrwYbdu2RThg8SYREVEFCSyMCgoK8P333yMrK0t1iRQnNzdX3XQZGRnqPj8/X90CxeEocN07UGCJgE1+zj+NggC+hxnobR7ItjcrtpV/2E7+YTv5h+0Uvm3l7/tYNHVxDf+tW7dOBRI5OTmoUqUKJk2ahCFDhhT7/LFjx2LcuHFFHpf/i4uLQ6AsO2LBV9ttaJnkwMs1ZqLjvi9wIKkrljW5P2DvQUREVFllZ2dj+PDhSE9PV3WWAQss8vLysGfPHvXCkydPxscff4x58+ahTZs2fmcs6tevj6NHj5a4YGU1ZeVejJ66CT0aV8OXnbcg4pcH4Wg2AAXXTQrYe5iBRJyzZs1C//79ERkZGerFCWtsK/+wnfzDdvIP2yl820qO3ykpKaUGFmXuComKikKzZs3Uz126dMGyZcvw5ptv4oMPPvD5/OjoaHXzJo0QyIaIsDk/isVqQUSUMxNideTDyhXTp0C3v5mxrfzDdvIP28k/bKfwayt/3+Os57GQmgZjRiLkWLxJREQUMmXKWIwZMwaDBw9GgwYNkJmZqeok5s6di99++w2hxpk3iYiIKlhgcfjwYdx0001IS0tDUlKSmixLggrp3wmfy6YbJsjizJtEREThG1h88sknCFcWV8pCZSzcXSEcrkRERBRMprtWiBrjwq4QIiKikDBPYGGY0ZvFm0RERKFhmsBC5yze1C9CxowFERFRMJkvsFDFm/pFyFhjQUREFEymCSz04k3PrhBmLIiIiILJPIFFccWbZZuxnIiIiM6CeQIL4wRZesZCfnPYQ7hURERElYt5Agt3zsJQvClYwElERBQ0pgksfBZvCg45JSIiChrTBBae1wqJkMucOh9gYEFERBQ05gksXPfuWk3OvklERBR0pgks9MhCc+YsDBciY8aCiIgoWMw5j4VxZAgzFkREREFjmsDCzbsrhBkLIiKioDFfjYX+AC9ERkREFHTmCSz0GgsWbxIREYWMeQILeBdvRjrvmbEgIiIKGvMEFq6UhTtjEcEaCyIiomAzT2Dh/QC7QoiIiILONIGFrjBjweJNIiKiYDNPYGGc0lswY0FERBR05ru6qZ6yYPEmERFR0JknsPDOWLB4k4iIKOjME1i47jmPBRERUeiYJ7DwHhbC4k0iIqKgM01goStydVNmLIiIiILGdMWbhV0hzFgQEREFm3kCCxZvEhERhZxpAgsdizeJiIhCx4TFm67IgsWbREREQWeawKIIFm8SEREFnWkCCxZvEhERhZ55AgsWbxIREYWceQIL1z2LN4mIiELHPIGFK2VROEEWL0JGREQUbOYJLLwf0LtCmLEgIiIKGtMEFrqixZv5oVwcIiKiSsU8gUWxxZvMWBAREQWLaQKLwvmx9BoLdoUQEREFmwmLN11YvElERBR05gksvGssOI8FERFR0JknsLAUN6U3AwsiIqJgMU1goWPxJhERUeiY8FohXsWb0hXi7h8hIiKi8mSewMJ7uKlevClYZ0FERBQUpgks3LyLNwUDCyIioqAwccbCEFiwgJOIiCgozBNYeF8txGoFrBHOn1nASUREFH6Bxfjx49GtWzckJCQgNTUVV1xxBbZs2YJw4i7e9BhyysCCiIgo7AKLefPm4d5778XixYsxa9Ys5OfnY8CAAcjKykLYdYUIXoiMiIgoqFx9Bf759ddfPX6fMGGCylysWLECvXr1QljNvCk4lwUREVHFqbFIT09X98nJyQifjIWxK0S/EBmLN4mIiMIuY2HkcDjw4IMPomfPnmjXrl2xz8vNzVU3XUZGhrqXbhS5BYrdXuD8QXO+toiwRapMhj03C1oA36si09smkG1vVmwr/7Cd/MN28g/bKXzbyt/3sWge1Y7+u/vuuzFjxgwsWLAA9erVK/Z5Y8eOxbhx44o8PmnSJMTFxSFQ0rKBl9ZEID5Cw4vdnEFGn01PIDFnH/5q+hiOJBYf/BAREVHJsrOzMXz4cNVbkZiYGNjAYtSoUfjxxx8xf/58NG7cuMTn+spY1K9fH0ePHi1xwcpq04GTuOy9pagaG4llT/RRj9k+6QvrwTWwX/c1tGb9A/ZeFZlEnFJ4279/f0RGGmYnpSLYVv5hO/mH7eQftlP4tpUcv1NSUkoNLMrUFSIxyH333YepU6di7ty5pQYVIjo6Wt28SSMEsiGiXK8lNRbu142MUXcRKJA3DNh7mUGg29/M2Fb+YTv5h+3kH7ZT+LWVv+9RpsBChppKF4ZkK2Qui4MHD6rHk5KSEBsbi1CKinBWb+bZHYUPch4LIiKi8B0V8t5776kUSO/evVG7dm337dtvv0WoRUfY1H2ur8CC1wohIiIKijJ3hYSr6AhnjOTQAHuBAxE2a+E8FsxYEBERBYVprhWiBxYeWQvOvElERBRUpgksoiRD4R1YcOZNIiKioDJNYGG1WmCzOLtqcvXJsli8SUREFFSmCSyE3huSm+/dFcLiTSIiomAwVWAR6bpeSF6Bd1cIAwsiIqJgqBwZC16EjIiIKCjMGVjoNRYs3iQiIgoqU3aFFA43dU0/yuJNIiKioDBlxsI9rbeNNRZERETBZKrAIrLYrhAGFkRERMFgqsAiwqrPY8HiTSIiolAwV2Bh8RoVwuJNIiKioDJ3Vwhn3iQiIgoqkw435UXIiIiIQsFcgYX3cFN2hRAREQWVSbtCWLxJREQUCqYKLDjzJhERUWiZMmNROEEWMxZERETBZLLAoph5LDhBFhERUVCYKrDgPBZEREShZa7Aoth5LJixICIiCgZTBhZFaiyYsSAiIgoKUwUWNldXSH6BV1eIww44XI8RERFRuTFljUV+geaZsZAsRl5OiJaKiIio8jBnV4h3xgJAl7G/4MtFu0K0ZERERJWDubtCDBmLKOTj6R83hGjJiIiIKgeTdoW4AguLBbBGqh+jYA/hkhEREVUOpgosbN6jQgzdIZEWBhZERETlzVyBhUXzLN40dIdIVwgRERGVL1N2hfjKWESzK4SIiKjcmSuwsHrVWAibXmPBjAUREVF5M/eoEPWgM2PB4k0iIqLyZ9KMhaHGgsWbREREQWPKjIVHjQWLN4mIiILGnIFFgQOapnlkLNgVQkREVP5M2RUi7A79eiEs3iQiIgoWcwUWroyF57TerowFayyIiIjKnSln3hT5dnaFEBERBZvVbB9GLg8icgsKnD+weJOIiChoTBVYSFAR6UpbuIecMrAgIiIKGlMFFiLSNTQkXx9yGqEHFuwKISIiKm+mCyyi3BkLz+JNTpBFRERU/kwbWMhcFgovQkZERBQ05u0KYY0FERFR0JkwsHBlLPQaCwYWREREQWPawMJdY+Eq3oyEa/gpERERlRvzBRau6Tfzisy86cxYbDyQgWs/WIRlu46HbiGJiIhMyrwZC/dwU33mTWdgcfNnS7F053Fc8/6i0C0kERGRSZl4uKl38aZzVMjhzNzQLRwREZHJlTmwmD9/PoYOHYo6derAYrFg2rRpCOsaC1dgEc3iTSIiovALLLKystCxY0e88847COfhpu5RISzeJCIiCpqIsv7D4MGD1S1c6RmL3GKKN4mIiKj8mK7GIi7Kpu5P59l9Fm8SERFRGGUsyio3N1fddBkZGeo+Pz9f3QJFf63YSGdXSObpPPWYRbOqD+nrImSBfP+KQv/MlfGzlxXbyj9sJ/+wnfzDdgrftvL3fSyaprmGT5SdFG9OnToVV1xxRbHPGTt2LMaNG1fk8UmTJiEuLg6B9uNuK+YcsKJ3bQdaV9Vw8sBWjM99HjsdNdEn7z8ez32zB68fQkRE5I/s7GwMHz4c6enpSExMDF3GYsyYMXjooYc8Mhb169fHgAEDSlywM4mkZs2ahbYtmmLOgZ2oWbcB3lu+Dx0sUTIkBJGWosWbQ4YMQWWjt1P//v0RGRkZ6sUJa2wr/7Cd/MN28g/bKXzbSu9xKE25BxbR0dHq5k0aoTwaIiHWOQrkdL6zeDMPkcUON63MK215tb8Zsa38w3byD9vJP2yn8Gsrf9+jzIHFqVOnsH37dvfvO3fuxOrVq5GcnIwGDRog1OJdxZvZruLNPNdHZPEmERFR+StzYLF8+XL06dPH/bvezTFy5EhMmDABoRYf7fxIp3K9AwvWUxAREYVdYNG7d2+cRb1n0IabZuc5ayryNGfqJpKBBRERUbkz3TwW8dE2j4xFjqvGIsLiQCJOhXTZiIiIzM58gUWUMwmTnevMWGQgHpsc9dXPl9qWhHLRiIiITM+0GYssV8YCsOCHgl7qp6ts8z2e63CEb5cOERFRRWS6wCLOlbHI0qf0lkmzCnrCrlnRxboNTSwH3I/n6dcTISIiooAwXWChDzc1JiOOoCrmOToWyVrk6ldAJSIiooAwXWARG+kMLLxNdnWHDLMtgBWuybMYWBAREQWU6QILq9V5ETJvsx2dcVKLRx3LcZxv3aAey7UXneabiIiIzpzpAgtxfTfnKBAjmdr7p4Lz1c9X2+Y5H2PGgoiIKKBMGViMvaxtid0hA63LkYBsFm8SEREFmCkDi5hi6izWak2w1VEXsZY8DLEtYcaCiIgowEwZWBTPc04LBhZERESBVckCC2BqwQUo0Czobt0C64m/Q704REREplLpAovDqIY/HR3Uz8nbfwj14hAREZlKpQssxIrkweq+5s5pMq+3x9/2nchG1xdm4T+ztoZo6YiIiCquShlYbE66EBlaHGKzD+DbyZM8/jZt1X4cPZWHN2dvQ7ZhWnAiIiKqxIFFpM33RFmNU+KRUKUK/lfQQ/0ese5rn9caEbM2HirnpSQiIjIX0wYWUTbfH+3xwa1QPT7KPafFYOsyIDfT/ffMnMIsxeaDhY8TERFR6QpPz00mKsKKrLzCKbv/eKQ3ChwamqVWwY4jp7BKa4Ydjtpoak0DNv4InPMP9byMnHz3/5zIygvJshMREVVUps1YtKyV4P555j97qS4QCSpEclyUx5wW2sL/AqcOq58zThcGFscZWBARUQh9NP9vvD1nGyoS0wYWr13bCUPa18IPd/dAi5qFQYZIjpfAAvi+4CIc0xJgOboF+LgfcHS7Z8Yim4EFVR6apmHn0Sw4HFqoF4WIAOTkF+DFGZvw6sytOHYqFxWFaQOLulVj8e6ILujSMLnI3/TA4giq4uq8sShIagic3A3tk/6IP7zS/bxj5ZyxmL4uDee9OBvLdh1HOHht5hbc//UqHlgqqc//2oU+r87Fv3/bHOpFISIA2XkF0Fy744MZOagoTBtYlKSaK7AQO7Xa2H/V/4A6nWE5fRwvZjyBgdal6m9/H8nC7mNZpb7eoYwcdbZXmvEzNqmDt+6eiSvVyjJqUmEwEyoSTLw1Zzt+WnMAa/adRDjxp23p7I3930Z1/8E8zkhLFA6ycgsHExzOZMYirDlrLApl2Kri4JXfY1ZBZ8RY8vFe5Ju42far+lvvV+eWeGD735oDOPfF2XjVEDBk5uTjyncX4oN5O9yPHc7MUTtsOXinpZ/2eI3cMLhmSbqhtsQeRhmL53/eiPPGz8bRCpQGJAom2TYk22TsxqXQnqTNWJdWZD9/JrIMcykdZsYivCXFRqKloe5CosJfNmfirvx/4it7X1gtGsZGfoEnI74CNAdOZOd7fLlr951UmYe/j5zCU9PWq8ff+aMwiJi4ZA9W7jmJ8TMKU8oHThauFKv3eGYEYiJsWL8/Hf/8dnW5HEAlMPp1fZoKdIoLko5lFb7vacNoGl8Opueovj9/SXs98+N6nDyDmpVPFuzEoYxcfLlod5n/l6g0f247gqFvLVDbX3mQfYs/Wc+z8dB3a/DsTxswZsq6cn0f8s+C7Udx98SVGP3DOqRn53tkHcoqK7dwPyv7wYqiUgYWVqsFP99/AVq5Ro5sOZSJxX8fQwFseMp+K/6df716/I6I6VgYfT8cU+8G1n6PgszDuPydhbjs7YUq8yA/2wsKsw13f7UCH//5t8fIEt2Bk4XR66q9noFFdKQVl761AFNX7cdTU9djyd/H1GvJAbysfK3E3yzbi7u+WqkCnfUHMnz+n8w2qivpzEfqQSSD8ORUZ0DlD2mvLxbtxgu/bCrxeRKsFBfU5NiLD2Tku3vwm1WYvGKf38tE4U8yf1e995eq+ykvN36yFOv2p+OOL5aXehaqXw1ZpvsfPXmtX7VIV777Fy56ZS62lOOcOPO3HlH3v6xNg9n4OoGZsnJf2NSl+bLneLb7e+n43Ez0fW3eGb+WcfZn6XKvKCplYCEibVZUjYtUPz/z4wbDLJsWvFdwGR7IuwdZWjTqWI4jZftkYMrtsL3WHJ+c/ifGRExEL+saICfDY66MGesPqoPnEUNfmL5i7D9RGFis3H3C4+Aty6LbdjgT1324WL3WuP9tUI99v3yv2pmVVmsgG1zbZ3/DD14HWOMMotJPtz0d+HrZXo/nGIfWZpy2l9g1IX5YWfaD+Lp9xZ8Vyk568Jt/quJBfQeebwjaVu05iVs+W4o9x5wbrdFNnyzFtNUH8NS04s/YpO1CUZS64YDzoFXagUUCT8nslPYd7z2ejccmr8G2Q8GZvE2+72d/XB+SOpexP23Eit0nVN2PMYAX8l2u2H08YNPup5USxN/06VJc+PIc9X4y3f+3y/diyc7SD25y0iJ+23AQ4XbA9rfrRL57Weeec9XgBMuGAxnoMHYmXv61MPMrAYVkaK55f9EZB6vfLduLU2eRRSjNCa+if6mj815//cWMRQVknL5b16h6HLo1qobb7hmNLrnv48a8x/G+/VJscDRUf29j3Y3/i/gFX0T9G+tibsfsqIfxWuS7GGn7DZ0s2xGNPHxvOLAfc2UC9hsyFst3n1AbjPHCZ76WSYb+yaRej05eq3ZmsqFJRuLaDxbh3bnbiyy7bHDi4e+d96/8thkD/jMPK/eccD9HunU+32bDMz9t8jg4GYcy6Tsc2an3fGkO5mwuDEzWFhMcbD2U6fOgb1RQwsFJRuDI55WNUO696z6W7jyOP7Ycwd0TVxQ9k3RttDn5Dp/Bg+wYpc0kcDnTDVyCRfkuymrkp8tUYHfrhGXqd8nI+Op7Pf+lOSqz03jMdHy5uPhuHyn4/W75Pgz/eEmxz5HPK+tbccGA/O22Ccvw1/aj7sd8fTbp9pOuqM8X7caIj5eorj9/yXdnXOe9SfAoQVJJJBPlawcrpqzaj6veW4T7Jq3yOFieSXdbaXLtBSq9LTv2v7YXLtP2UtrDePCKi7KhvNishZcv8DcA7P+feTjnuVl+HWB3H8tW69ynC3eqfcOZThwo7SiZxamr/Dsp+ejPXWrbfnduYTfz4h2F7X8mJwqyD3jsh7X4esken3+Xk5myBtEyuu/eSSvd2eJjPtrHe/39a8dRbD9c+smBMXA+kukMfqW7/Ez2RcFk2pk3/eFdz9C2TiL+N+oCd3fJsO7N8fXSaHWZ9ZcAVEc6zrduwAXW9TjPuhENrYfVzJ1NkYarbAvU/+VpNuzQ6iAdVZCpxSLul++B5Oro/ncWYm0aMhGrHs9EHE657jPtzvsMxHkEGdERVo8uFMkqyAopB1m53dO7WbGf7dr3F2Gpj3ShTFOeke/cEcmOv1ZSDG6bsNzjuXpXzgPfrFLPuXXCcux66ZIi3SxywTYJAs5tkoybP1uGanGRWPR4X9V2vjgMG+yuo1mqPuWf/ZurIcHG70JGpcgcIilVPItshQRXRsYp2MWpPDsSY5yZKN3J7Hws2+UMrnYdy0KzVGcXmByEqnoV8nrvZPZnyQHuOG78bDnu7NUETwxprXaQcsDdfTQbz1/RTs3yqj6fQ1PLXb1KtPs19M+lH2Tv+mqF+g4/v7U7zm+aoh7z3kk8PW09bjyvodfnTscfmw+rtL0wZsWMJHB64JvV+GVdGl69piOu7lKvyHOkL17StLM3H1bfqxzkJVtiJDtX/Wxb/LXjmApYp97Ts8jryZn7Pq8ygkFvzFdZgKVP9kVqQkyR1779i+VqGabccz46N6jm87MYi5ozc/OR5MowCilWFPIZjN0OUs/wxW3d1TolO2UJ6nq3TFV1VWfKmG00BrvFBUby+UZ9vQo7j2T5VaD9+8ZDaju6/cLGsFh8bzslkaBF3w4kI1kz0bO9fQd1zs+08UAGujcuOiTfyHigHPLmn9h34jT+fKwP6ifHlWk5v1m6V2UW5TbsnKLrpVG+Q26FbSbbXHSEDQcMQbkEOSVtv95kG5JlFwt3HMUdvZq4t1tp9tP5BarbQuY9ku3TXxLsixapCXigX3OfEysa19/th09h+EfOEwPZ/kpizIhLYLspLQND/vsnhnWqi9ev64RwVakDC/3MWNenZarHQTHXq3/vGJLwP8f56tavdSpyM47AlrYKHSx/o6N1h7qlWDLQ2mLoZtixEtgBDJGNspR9W64WiT32VOyKrIVdWk3sSquFKZM3op4lCie1eBw7mY49R095HPhUN0peFnByD/raVqEOjqC+5Qjq7T+Ch6IysdHREEsdrbDM0VItvxwgjHUVk5bsKRKAyBlC2zpJHmcmsvF5B2IPfrva+cPswhV/5saDyCvQcFnHOkU+nwzflQPyLec3wtj/bVBngXKTjct4oHxs8lp1P6qP78BJlkX/no57naFKsZTNYlHdQgPb1kJqYozHmfO0VQfUwVm6JiQ78srVHXBN1/o+32fcz5vx7doIYK2z//3D+X/jgmYpKvugj5yRlPj3d/VAt0bJeOT7NepMWoLT9vWSfB5w5rn6w0d+uhTb/jXEZ4Dry6Pfr8XGNN/1MUb/nbNdBRVi8oq9PgOLHYc9z7Tv+3olftvgecE9yf54d99ItkoyZQnREbihewNE2KzYfDAD//hU2icCd1ytqZ2mfDV618LyXScwpH1tj9f5df1Bd13A9LVpxQYWxrM17zNr45BxCabk69DbRwKrBaMvVt2Ssn5f3CoVn97cDWdqryGw2G0IJorr3pL9ine9Q0ln+RJkiQ71knBuk+oe63dpJCg1ts2avScxoG2tEv/HeOAr5pJKypNT18FqseC8JtXdj+kHZukKfbBfixLfRzJIEshLobxcRkEKTL27JWZvOoy+rVOR4HUy8Po6Gw5kFwaNsl7JPsnY5vI5yhJYSAZWV9MV7ErAcsl/F6BBchxuPr+RWm/l9sTUdRgzuBVkK5fC3vMaVy/1O9HnmTiRXXLGQr4j4/dnzDh5yzZ8t0dO5eKtOdvUvBayn2FgEabkYDDHcMbTv01Nj7/f06cpft1wEB3rVcUiQ1p2yRN91VnBi9M34cP9GuaiE6DWGw11cRRNrQeQgNOoYjmN69onoUG8HT8t2YwqOI0ESzYSkK3+Js9JUPfZiLXkIdqSj+aW/WiO/YULcQB4QD8Bng4MA/BctAX5iIB1fBQKrFbY8pwb2yc+ApfzrJtwK5xDZ7c76mDp8VZYYm2F9Vpj5B+Jx8a/j6GBxbN7o0CzYsyk+YiOSYQeem09nOmjsFJDPHKQiGz1uXIQhUe/WqCyMs1qVEGbOonuegljn73sOI2vJTsgX2fgciAvrjiqUUq8z41YzigleJnw1y5VzDnt3p74c1thyv/tPzy7kKSbqbjA4tvl+3z2tXv7avFutS7Jxi7en78D7wzvXOR5xtFF+QWaKvSVqee9MyzGMzSdP0GFHLzeNXw+ybRIBkzWVZloRz9rN2ZI5LvwDir0Myzp3jKS/3v5V+ew6r+PZuGS9rVxtaGve8Wek7jhY2eXj06+VzkYGKfYN3ZxrPUajfH+vB1qCPfwcxuoZdadcp2Ry0E0PsrmcZFBOdgbuxr0g7gEFULfxvUUt2S9GlSPw6cLdsIfxszEHsMIj+LqXLYeKtpFMnPjIZWJG1DV83Hj9rHt8CnUTorF9R8uQpdGyXi4fwv3el5cCl4CaGPmXtb50gILYyDrnfEzdo3K6DYR66Mbp7hUvGzLMZHO5z/83RoV5H5wYxf835eeXZiy/UtmTrIXV3SqgzeuP8fj7weyPQ+2m9MyUSU6AmsMXbG+DuATl+zG10v34OObuqlsrJAs1kd//u2ReZLMph5sSNAiNwmWdbLuSMAqj8sIv5ev6oBruxXdTxi7TfT44JihEN79foYAIdtwwurMzEZ71JTJyaIETZe/s8CdWdLb3FjQL+uOd0iycPtRtV6OPL/RGWW/AqVSBxYvDmuPzxftwvJdx1ErKRbt63qeZUrKfONzg9TKM+iNP92p4RquFUHS1ZKSLUxzWrAfNbDfUcP9GnsyqmPRatmRdleXcv/y1nNx/YeLiyxLJOyoaTmORpZDaGQ5qG4NLYfQ2HIQ9S2HEW0pXDFlOGw08gF74YEqNzIRu+zVsdOegn2a3GogU4tDB+sOdLduRmvrXjSzHlC34Zjj/KfFwA1yX5i5LyI7OhqnEIuICYmIiE3E/6JOqUAi0ZINCTtslqI7mHzNhoLPqwFJqUB0NbwbmY8MLc7Z7aPFIX5VDVwSWQVVrfnIRgy+mngQtZOi0cu6B1Z1jqCpDUbTLDhticZpROE05D4ap7UoDHn1V/RqnoJutSPQoqqGzpathQHa2r8RueJv/MOmAWnArC/mY+/Wo7jB5nxVO2wqM5SHSOS6bhsWR6JtgxqAxaoO+FIsu/lQJtpanGc4skSnXF1YpxCngrqSCv8kY6Lv5ErKkJU0SkbODJvWcF7bRp8t1nimKbU8mXvXIyF7H3Bip7rWzaGsKuiJXGjVGyINqdh2LE91vUhAsXL3Scx7rDeqx0fDbkgx933dd8X6Dyv2q51qcWTHKxkoo9dmFb2egX6W+tKV7XG9a8dtPOuXLr1znpuJ16/thAibBS+5hmh7jzrKzLVj0Y5juPGTJbjroqYew6O3Hsz0SMtL+tj7oH/xa3ORdjJHHWzke5Baql1eNUHSvvIe/dqkuoM62ZHrQ8q9lz0tI0cFgHJwnrBwl6ral/S8dzCtB8NyO5Fow3C7A4dOZaNetTiP7/SdP7a73+vAmgMqwJJ9lARZJaXgvQMY+W68/0f2YfqBRs58fQUWxkyJcZSYtIk3va7JSNb3sT9tUCdoF7Wo4c6cvfJb4Rw/OvkOJKgQcj/usnburgJfNQ6y75XAzBjQHM/KV105o75eiccGtkT3xtXd642clDwztI060bj98+UqaDOSblAJzIwHfO8aIuk+1bdZCdh8BRbG4Fea94WfN/o8CfCY6MowukOCEAksJHsjBakSaMx5uLdqO2NQ4W4HQ8bm8Slr8eLlbdy/SyAktVCicY0q6jsIFYsW5HLvjIwMJCUlIT09HYmJiQF73fz8fEyfPh1DhgxBZOSZ96cWR/qM9cuoG/vFZMdwxTsL3UOMSiKXa1/xdH91purroHJek2R1lunNAgeiYEeDpCgcSc9EJAoQgQJEWuzq/qiWhAwUf2YjO9EHe6bg55+nqiCju3UTmlkOqA1B//adh3IneS/JnvhLAolsazwiHLmIt1ScyuUzlaNFuoKkWBUYRUREonntqli+NxMOzYpqCbFIrhKLzWnpiLHkIRa5iEE+asU5kHs6CzHIU+0tgVK25gyYsg0/58MGm82Gfq1rIyLCBjkvkZ2vBRrqWCTDdBi1LSWPSNAsVux3JGOPIxUHkayCo9qJMci329VOVV5LbiWxIxJZWpR7+XI0532BLQb2ggK1pBLg6OtLFORWuAOVd5Bl1+9Hnt8EMTEx+HTxARzKciAPESrAk3t5jrSTZMBiLc77OLlZclWw2bFpfczckY0MR4wK8pyBXpwKDK/q2hD1qidg/K/bYYdVDRsvgFVtG7I8sp3oyyb3sv04XEGmBInyXGlz59bkzISc27g66lSNxqYDGdhsyEDExMTiyGm4l/vzOy7Efd9vwP4TOa52cLaH/n76dmTXnO8ldVNVqlTBzpN2PHNZR9SuGovbXF0hxu9DtkZ5jWjY8d3tnRFvtQMFuYA9D9AKYI+sgss/2YB0xCNDi0dqSgo6N6quiizlBOl/9zlrxYTUz/zj4yX4Z/8WuKVHQ/ywbCeembpKncw8M7g5ruxYAzM3HMSLM7bgsUGtMaRDXSzedQL3TFqj2knaU9pHv5elu75bfbx0VQdkZOfgg5mr0DLRgY9/X4kEZCEJWYhFHk6gCo5piTiGRHV/Gp61H1Y4UA2ZqG7JQMsqufjP0LpqTciyJuDaidtxQkvAcSQgB9G4sHmK2td611gZSZeCHni0qZ2oJiU0BkjFkbXTub7lqkyy/CxtU6Nqgvqe5Hvu1LgW3hh+LhARDeRmAMf/VreMA1uxcOlSdSLY1HYE6Y4obHI0xEatIY4ntMQfJ2vib6023h7RDYNdXYIPfbvand2cdMe5qtZKRtxIcaz48rbuKqiQ7hjfNPfyPjmwKeKPrEfvXj2x6UA6Rn+/Uq3FN3StjeFXXA7YIkNy/GZg4ScZ6iijEnwV3Py4er/KXDx3eTs1H0VJ5H+l6NFdn2Dw7NA2GOc1pOv+vs1xKD1H9eX7IsVvUrRWHEmn3nZhY6zee9JdMORt2Dl10ah6PCIjLO5Ut2xY8TiNV4Y2wmdz1sGenY4utWzYfCgL6Vq8KjSVHdrt/Tvizovbqbk5hr37lzrQyM7iqtaxyDpxCMcOp6GaJdPV7ZPtynacRrWIHEQXZKn3EFarFXKipx/u5F4OKHIQlgNNcqQdkY5cRDgKo/0CzeLOgsjy6D/nwaZ2zM5Dmn7v/DlCdvay07fYCw+K6vf8ImlFnRzaZTnDLWjK0GJx0FYbLVq2Q25sDSxZvQ417GloHnkUEQVnP+sfVRwqgItOxKEcmzo416gSAYvDLmkI5OTlAg4JCxyItPg/sV1x7JoVDmsEoiIigXz/J/+SwFkCDAnBZJ+QjEyVfS2NZCklSMnWYlQwGBUVhYw8TQWC8rssj2y7sr+wWhwqYJHPqn53/Sw3OeA6wyMHbBbnyZoEr+W9Xedqkciu2hzV5Hhnz8HeIyfgyDutgs7kKOdyZNqtyHRE4rQWjYQE2VtGY0+GfEYb4i05KhurZ2WlS91XpriIR7YDVWqE5PhdqbtCyuLZoW1xKGOlGhng7fJOddXNqF/rmmqcvbFf/ckhrdV9dR+jHcQlHWrj9ZlbkZoYrdK0En1f1rE2Tuc5ig0spP6j6GNJqqpZovtzGyerIjv9wmu+DO1YGxe3qqnShhMX71HFjnJ2de+Q7ujfswkWHktQNQtLfcy/U6dGdZUDPKdBNSx7sp/qP7/v61WYczIRmw/FQdMa+35TV7PI5ey9uwi8/fuq9riuWwPVnle/t1AFG7IjSUhIxKHMwA8vlNSkr4JK2Sk562ScG3iHGhYcOnpM7ahG9W6CD+dude3QnDsx+Ul2ovpNdpCyw5Cf5TlyxiFn6XJWLj/HuH7uXC8B6/adUDvGqzvXwZq9x7HryCkVHB3UkrFHq4ndWipOQOoWLPhHdANYNQu+yN6t5maZ9UAv1LCkY8ynPyHn0A7UsJx0h1bOoK2ww8mYqfI8Y9ZUcBlnycOoC+tg4p+b1Vlo7TgHWibbsHp/prs7KalKPPZmaurMTtYbZ1Do8ArqNOeBTeVB7Iix2tE0OQoHjmWoHas8X7I/Azo1wdL9uVh7OB9ZWoxqL/lvCez0uiS51+uV5H+rxVhh1ezIyZWQUrJ5cuAoQIFmc2dFcvXsiCYHowj3dyQHGFkelQF0HXiMbSI/S9++tOuBE1nObITKztjdGQU9K5Gr6e/lzGbkqS435y5Wf309y+hsB8+DvLS+jDCS4mc53dOXPSIqBidzLepnOWM+bdfU50+yuLIDljznN5qbjjr6ohs2KZUnKCZqdmZuIlSQLq8hZ+9qkJPDUexBP8IiZwB5QH7htidz/kj2JKV6KixxVbFodxaqWk6pbEQKMlQbqfXc4jw5MzquSWYjCYkptXEqT0NE7glE5Z1Esvo/u/p8sThe+Bns5TNRgrSB5FpOSX5Ri3BlulwnHvJdu7qjJauVHVcPW+2pKKjaGNP3x2KXVgtatcY4efwIWlv3YFjt4zg39gBy9q2BWovTNwKu8hDVoWL13A/Klpzg3GCALOeOtmEpI5QdmjPrJidl+ZYoZBcUZpcklKqRb/fKEQUPAws/SRHV9AcuLPV57/+jM75fvk8dDKXAbe6Ww7i7dzPVNyYHfOHrIC+XeJdhebMfvkj170pfrQzzkjoP74r4t4efowKQp4e2Uek/yTjIrJ267+7qoV6jZzPncEYhVc9SzS991bViNRw87dxKuzaspkbDCOmLXzC6j3o/Odh3cAUtg9rVUoGFLxJQ6GokRKNJDWeXjAyL8ueALe9fUmAhQ4Cv6uwc2SDL07d1LbXj7d6oGv5xXkN0em4WSiOjNmTYrowWEE1rxKNrw+RigzUpNpN+8nsmrnAHhvWqxaqd/f6TNqRrztqH+Nhq2BKZqdq0tdYMMxy+RzdIn/MV7Wur/nOZI0WGtXl0eXntv8fffQl2/7ZFFZq6pr8o0VeLC8fk/+uK9qihhhvG4HTNrpiWVnR0Tkl6NquOhYa5GuokxeDBgX3x7B+/qN8vrpOK8Ve2x50vuoYCAbi1dUN8+lfZplxvXTsRN3SvryanE90bJePFK9sjMrUK1s/bgX8bpsMvlR+xpQQGMuz4TDzet5VKxcvoAV+qxUbghBqGWvToHRNpVYWmGcUUSRqNu6wtburRED3+5XVtnNziP6uMTvt4RAcgJx04fRKX/ec3dQYvB5w7L2qGVnWq4u6v16ouOnlMD/70mwS/xXOGoBIIDW2filnr9qmf5VarihU/3dMDD0zdjl+2ZavgRIKiLfcNUrUcI8f8YigqdQZCH13VCK9MWaC6B49LF4mWqLo65GCoHCj6/hJwJ6vsRoYKbBOjNLSvUwVrdh9RS+9cHrsrJHKGs3Lg11z38pjcy2eXQDMhLgZHswtUKCvLnKW6IJ3daxIQynco9Sk9mlRXJ0g+u6RTElFgseHv41mAcTenNpsk7IlujZdGDVAPPfv9KixeuRItLXtVcO2s64pSWQw5wVDvKd0rdjmxcHabOgMp5++SWZVpCZKqVsPmE84Mbbsm9XD/4C645pNVOHHajvgIDVl257o3qG0tVZgs3WH9qzj366HAwCLABrWrrW5C5jOQ0QKiS8PCg44U0BmDBJmRUvo/hQyPFFLIpBekyRmTx3u0rYVLOxQeMF66qr3q6tAP0MbRBMaJt+Y/1geLdxzB0S3LMeNEKpLiojD2srYe1cPyswz90oMKYbyuivP1rapgtUlKvLo8vZE811dhnAQtCx/vg5ZPOUeo6Lo2quaeUCwhJsKjmGzqPeejSUoVlXHRq6U/Huk5bFCmZddrX3T/veEcRFotaqx6tbgoFbxoDaq5AwspjJz9cIdiAwsJZqSyfcnjvXHbO7/hkJaIT27upgICmWhLP0DJvBySXco8YncXqnkHNKkJ0Sqgio+OUMMuZecrQza9a2lkvgcZetmwujMwu7t3U1Xd7muyneLERtrU0D1fQzKNyyRFf8XNg3HL+Y1xZ6+majisHiwaSVAmn0lq/PQ6usHtauKrxbuQ57DgqUtaY8rK/bird1O0qFkFL/y8CVecUxcv/LIR8VERqJkYrQreJPCW4ELWWRnC27d14YgsyWL58t3/9VDtr3ugb3M1cZw/HhnQ0qMIU9ROilGFt7IcEnT7mvfF+flqqcBchoPqE8TJOiLB53Xd6qthis2enFHk/97/Rxf1P7KeyLors8rqxX6PD26l5iXRZ++UbVoq+b3neylNq1qJzgOTHESqpOLvyJ3uE5FH5sp3LCN+PEe7+c+Chwe2wj/ObYi4aBt+WFf4GaVu98utVvy4TdZP5/4pJsLq3pd0ql9VzZarv06LBnXQo1s3dEiLUycpsh5kns5HgUxYUcL7Sxbry7v6qP2NXC9oxLkNVZD44YeL8WC/5mooqLStvg/xzoDK+xhnrOyaUg2rXcNOm6VWwV4fBcpSGC778KfV9Y3yPcIsCQq2HS15ltaahnlE4mOisVurpW7F8o53DV+/zOUjmevxQ9rj26+cxbp9ajdEs3qpaFu3qhqurwcVMnLlys513fvLUGJgEQJSmS7V3lViIlSAYAwSiiMjSmTEgvBecSSQkIN5aV0KcqCRM5zpO4HPb+nqdy2K/J8UnuoHuecub6u6TaR7wpss2wtXtMc/PnHWc/RqUQP1q8Xi+m4N1HLKgcp4UNMnidKfKyMNJNshOyZjNqQ4cqCUuTlkdlG5eqwcIPQ5NPRiKSH7OwkwZNZTfTiePE+mi76mSz21U5eqfgnA9OFyspO8uokDQ4ac726rvx6/GG2e+U393K5ukmrzHUeyirS9FI/pQaVOn0jr1p6N1dmcBEW3fb7cPdLo5p6F3UYSiEhmQEYKGIfYvX5tR5URk2FyN/ZoqIbWGru09GUX0n6fLfTMNJ1Tv6r6DG/P2a4yIt7DBhNjJagsHB2lj4R65tI2qq1kUjZpFwma9e9Rvqvnuxag+wV90Dg1EbdfWNhd+NXt57rbWoIR73VXuhi91fEKVnVyFibr7++bDuPRgS1VAaE/gcXHN3VVAZcUMco8JlLYKAdf1RY7jqlh0TJ6xpffH7rIHey9cEU7NTuqOL9pdTx5SWFFvrfODaqqTJ9Rs9R4rN3nPMWVLKOMbmn0+C9FRlkYvxMJ3iXz6W3h4xfjt/UH1esYyfpxp9fQTl/0kwOjJ4a0wovTPTNFEjwZJybTyforw0WNjJcmkFE+cuFB+YwSCNRJcn6nDw9ooYYeSyAlo4AkwH591lafGU6dtL8E58Z9xfpxAz1GQ+iBxdOXtlYT+ulk5I0xsJDtXPYBQrY/XyOf9LkxPr+lu7oelO7/ejXBrE2HioyG8lbDEIxXifZ/xlU5gfTOTks7SrbMuJnqw9MluJXAQs8s+hq1EioMLEKkuCFkxZENpKTAwXuCmUAzTg4jE0+VNDHNBc1T1MFaNnYZkmucH2Ti7eeqa6BIul1eUrIyEhzI2YicpUvW4tMFu3BLT+fZW2nkc8vySLePZCfk7LI4n93STWUBrnR1rfznuk4qSNI/iwQKpZHA48d7e6quJ8kyydBUfZ4M2cnLcmxOyyjx+5WD/72uyb++uLW76hrzNeZcAiC5/bz2AEZNWqWKe2XZ5X1kAitJs8vZskyaIwcAKR42GtKuFk5c1lZNEPXz2jTUrRbrPrDLsku9kOzQ5UqMepAgO0VZvuu61lcZnXtds7veekFjddNJoGkMEGNszu6i4uhBlT/0+Qe8yXwKr13bCct2HsdFLWsgwmpx1iS4DpD39G6q1jW5Nosc0N74fZsKRPq51r9/X9UBzwxt65EBlHVVeLe+BIbv/aOzO6gQDZMLf/b+vj67uZsavvh/FzVVQxOlS8Pb4wNbYvgny1AzIVplfYQEj3IdoH8aJpuSbIbMjiqvIRk8X4GFZAqN34dO1pfVz/RXFz4zzhIq5PvW54aRA7x3ECGBvexnJKsiQ/Cnrd7vEbg8fWkbNQ9NYkyEz64dmYFXJ5mDL29zBpXe26txvgj5vuQmF16UayT5IvuEksg6Pf/RPiqjGO91IJfgXp8YS/Y9UnNmzPDddkFjj+D8of4t3M/pWL8qlj/VD11f+F39Ll0MzVOruAMLyd7J8b6qDOc2XLG6pjFjYVjXpEtXv8aScQSLztf06lKPJ+uazQIVTMsQ5BHnOdvvnAaFWWXjyUA4YGBRQbx2bUd1XYTRg1v5/LuckckZpexsy4OcMegHEn9mu5Od+KiLm6kUspFMlzvx9vPUVMt6yls2fuPZvYw/Lys5WMjBpCSyU5M0v0427rLM3KeTHY7cxGWd6riHDsuBwJ8si/fOvDSS0erZNMU9wZXsaPRJiyQwe/nqjsXucPX0ugRt3mSn17VRsiq6lYtkyZh6/TuRegcpJvbOuuhkNkbvLqhAkaClONIGeqAgOtRNcp+BSgZHJpha/lR/9bvU4MiZuU7azbtbUffIwJbuC1tJICZBhXeXovHM3btWqE+rVHXTAwNfpL7m0Q52DLi4pzswkYOs8UArJBMj2TX5LqavP+hRP+UPWafnPHyR+lkyMlIr0K5uohphJhMotauT5DFHipwRS1Ao3Ziqa0UdBOuqblJjBkwOwld3rof5244UqT+Q2hyZi+JMyYyj13atp2Zl9Q5afHXtepODvvCexO++i5upywcMbu88+TCSzybBklyK4NWZW9W6Im1kJEGDcb1snprgnlBO1sMxg50F+X1fm6syl3r2QGecGVi6CfXAQoIXuQaL9/V0jF2M3tkPORnRT0hE/za1MLRDLfxv7UEManum3V3lg4FFBSHTHkv6szh6+l/S0uVB+sSf/nEDnrnUuSH5k+Ewnu15857ltKKSglsp2JWzw7IGFWXhq14ikCTrYyRBV0lBjxyIZZZFX1O3ny1jNkA/My9ush85WOiBhT5Ns854UCyNBFBS55ISH+3fdNpnOEi/XrzMK1P8dqF//uauuqahHWqrqyFLxqlBcqw6M5ZuhNLo16sZ2rGOqu+SmUklqPrlfmcBugQYugm3dC8yrbQsg6/2k+DKu4tHfHRTV58XdSwLORmRm1yIT/fD3T3K9BoScOu1MJJRkOD5/Ru7+HyuHnRK1508Ty9i9w7OJTMgQYCsa8YskARZOune0QOLToZMghSJf4C/VXeQbMOf3twV7/6xQ11jSL4POcGSTKtcxOyxQS1VXYp+Nep3R3Qucp0dI/nOXr+mA3pE7cMl7UtfJ4KJgYVJyI7Ae8hrIEkxp3QBUFF6sW5lIjtFmSBJn0OmvEjwImfKxZGgY+7WI6qmwd/raxSnpJ24TrouZII7udhUsLZryRxIN5iMPpqxLq1IEFgaXzUrknG648LGKhgu6VoVvkgtxe8P9VJz7ujdgGcbVBgDyteubo+np63FBzd2UxeTK6uv7zhP1Sb1aFp4jRNf9GyABFC3GOqbfBUN2ws0FbTIBe1k3Zcsjx78eRfkd2mQ7Dlq56au7m4LGdYvN51k1UT3xv1Ul6jMvCl1NTJLbXFFzN7iy7cX/IwwsCCisGUsCCyuaySYAa+vrotgkIOuFO35Kpg+ExJMlFR8WhoZBi81FDKypaQ5cs6EzN1j27dKzUR8JiT78Ngg391RYuzQNqoAWLrO/F0H9eSN1E+seqZ/kZocyRR/uXh3kS4zi8Xi0XVXWpAjXc5jXPMdVWQMLIgo7MhZ3rifN6iqeApfel1JoJXn9bNk9JVxBFYggl0Z5inFl8XVJFU2DCyIKOzIWZ4/Z3pE4UBNCuYqlKZymRiViIiIKisGFkRERBQwDCyIiIgoYBhYEBERUcAwsCAiIqLQBhbvvPMOGjVqhJiYGJx77rlYutR5JUQiIiKq3MocWHz77bd46KGH8Oyzz2LlypXo2LEjBg4ciMOHD5fPEhIREZF5A4vXX38dd9xxB2655Ra0adMG77//PuLi4vDpp5+WzxISERGROQOLvLw8rFixAv369St8AatV/b5okfPKgERERFR5lWnmzaNHj6KgoAA1a3rOiCe/b9682ef/5ObmqpsuIyPDfeGiQF68SH+t8rwgkhmwnfzHtvIP28k/bCf/sJ3Ct638fZ9yn9J7/PjxGDduXJHHZ86cqbpQAm3WrFkBf00zYjv5j23lH7aTf9hO/mE7hV9bZWdnBz6wSElJgc1mw6FDzuvF6+T3WrV8X8p3zJgxqtjTmLGoX78+BgwYgMTERAQykpLG7d+/PyIjw/A6smGC7eQ/tpV/2E7+YTv5h+0Uvm2l9zgENLCIiopCly5dMHv2bFxxxRXqMYfDoX4fNWqUz/+Jjo5WN2/SCOXREOX1umbDdvIf28o/bCf/sJ38w3YKv7by9z3K3BUi2YeRI0eia9eu6N69O9544w1kZWWpUSJERERUuZU5sLjuuutw5MgRPPPMMzh48CA6deqEX3/9tUhBZ3E0TStTSqUsKSHp/5HXZZRbPLaT/9hW/mE7+Yft5B+2U/i2lX7c1o/jxbFopT0jwPbt26dqLIiIiKji2bt3L+rVqxc+gYXUZBw4cAAJCQmwWCwBe129KFQ+cCCLQs2G7eQ/tpV/2E7+YTv5h+0Uvm0l4UJmZibq1Kmj5rAK2XBTb7IwJUU6Z0salytj6dhO/mNb+Yft5B+2k3/YTuHZVklJSaU+h1c3JSIiooBhYEFEREQBY5rAQubKkCuu+pozgwqxnfzHtvIP28k/bCf/sJ0qflsFvXiTiIiIzMs0GQsiIiIKPQYWREREFDAMLIiIiChgTBNYvPPOO2jUqBFiYmJw7rnnYunSpagsxo4dqyYbM95atWrl/ntOTg7uvfdeVK9eHVWqVMFVV11V5Aq1e/bswSWXXKIuZZ+amopHH30UdrsdFd38+fMxdOhQNaGLtMu0adM8/i4lRjI9fe3atREbG4t+/fph27ZtHs85fvw4RowYocaJV61aFbfddhtOnTrl8Zy1a9fiwgsvVOufTFjz8ssvw0ztdPPNNxdZxwYNGlTp2mn8+PHo1q2bmuBPthO5GOOWLVs8nhOo7W3u3Lno3LmzKsxr1qwZJkyYADO1U+/evYusU3fddVelaqf33nsPHTp0cM9D0aNHD8yYMaPir0uaCXzzzTdaVFSU9umnn2obNmzQ7rjjDq1q1araoUOHtMrg2Wef1dq2baulpaW5b0eOHHH//a677tLq16+vzZ49W1u+fLl23nnnaeeff77773a7XWvXrp3Wr18/bdWqVdr06dO1lJQUbcyYMVpFJ5/lySef1KZMmSJFytrUqVM9/v7SSy9pSUlJ2rRp07Q1a9Zol112mda4cWPt9OnT7ucMGjRI69ixo7Z48WLtzz//1Jo1a6bdcMMN7r+np6drNWvW1EaMGKGtX79e+/rrr7XY2Fjtgw8+0MzSTiNHjlTtYFzHjh8/7vGcytBOAwcO1D777DO1/KtXr9aGDBmiNWjQQDt16lRAt7e///5bi4uL0x566CFt48aN2ltvvaXZbDbt119/1czSThdddJHaVxvXKVlHKlM7/fTTT9ovv/yibd26VduyZYv2xBNPaJGRkardKvK6ZIrAonv37tq9997r/r2goECrU6eONn78eK2yBBayQ/fl5MmTakX9/vvv3Y9t2rRJHTwWLVqkfpeV0Wq1agcPHnQ/57333tMSExO13NxczSy8D5gOh0OrVauW9sorr3i0V3R0tDroCdkQ5f+WLVvmfs6MGTM0i8Wi7d+/X/3+7rvvatWqVfNoq9GjR2stW7bUKqLiAovLL7+82P+pjO0kDh8+rD73vHnzArq9PfbYY+pkwei6665TB2wztJMeWDzwwAPF/k9lbCch28jHH39codelCt8VkpeXhxUrVqgUtnHacPl90aJFqCwkfS9p7CZNmqh0tKTHhLSNXAHP2D7STdKgQQN3+8h9+/btPa5QO3DgQDUP/YYNG2BWO3fuVFfoNbaNTFcrXWnGtpG0fteuXd3PkefLOrZkyRL3c3r16oWoqCiP9pPU74kTJ2AWkk6VVGvLli1x991349ixY+6/VdZ2Sk9PV/fJyckB3d7kOcbX0J9TUfdp3u2kmzhxIlJSUtCuXTuMGTNGXalTV9naqaCgAN988w2ysrJUl0hFXpeCfq2QQDt69Kj6Qrwv2y6/b968GZWBHAilz0x2+GlpaRg3bpzqx16/fr06cMqOXHb63u0jfxNy76v99L+Zlf7ZfH12Y9vIwdQoIiJC7SCNz2ncuHGR19D/Vq1aNVR0Uk9x5ZVXqs+5Y8cOPPHEExg8eLDaOdlstkrZTnJBxQcffBA9e/ZUB0YRqO2tuOfIAeP06dOqHqgit5MYPnw4GjZsqE6IpPZm9OjRKsicMmVKpWqndevWqUBC6imkjmLq1Klo06YNVq9eXWHXpQofWBDUDl4nhUASaMgG+91331WIDYvC3/XXX+/+Wc6QZD1r2rSpymL07dsXlZEU1UnwvmDBglAvSoVspzvvvNNjnZICalmXJHCVdauyaNmypQoiJKszefJkjBw5EvPmzUNFVuG7QiSNJmdM3pWy8nutWrVQGUmE26JFC2zfvl21gXQXnTx5stj2kXtf7af/zaz0z1bSuiP3hw8f9vi7VFzLCIjK3H7S5SbbnqxjlbGdRo0ahZ9//hl//PGHx9WaA7W9FfccGTlQkU4WimsnX+SESBjXqcrQTlFRUWqkRpcuXdRomo4dO+LNN9+s0OtShQ8s5EuRL2T27NkeqTf5XdJLlZEM8ZOoX84ApG0iIyM92kfSjVKDobeP3Es6znhgmDVrllrxJCVnVpKWl43O2DaSHpSaAGPbyIYt/Z26OXPmqHVM3xHKc2S4pvSHGttPzkQqWnrfX/v27VM1FrKOVaZ2ktpWOVhKulo+n3fXTqC2N3mO8TX051SUfVpp7eSLnLUL4zpl9nbyRbaZ3Nzcir0uaSYZbiqV/BMmTFDV6XfeeacabmqslDWzhx9+WJs7d662c+dObeHChWrokQw5kkpsfciSDPWaM2eOGrLUo0cPdfMesjRgwAA1NEyGIdWoUcMUw00zMzPVMCy5yer++uuvq593797tHm4q68qPP/6orV27Vo188DXc9JxzztGWLFmiLViwQGvevLnHMEqp3pZhlDfeeKMaJibrowzvqkjDKEtqJ/nbI488oirRZR37/ffftc6dO6t2yMnJqVTtdPfdd6vhybK9GYdJZmdnu58TiO1NHyL46KOPqpEA77zzToUaRllaO23fvl177rnnVPvIOiXbX5MmTbRevXpVqnZ6/PHH1UgZaQPZ/8jvMpJq5syZFXpdMkVgIWRsrnwBMp+FDD+VsfSVhQwdql27tvrsdevWVb/LhquTg+Q999yjhjHJCjZs2DC1kRvt2rVLGzx4sJpXQIISCVby8/O1iu6PP/5QB0rvmwyf1IecPv300+qAJ8Fp37591Xhyo2PHjqkDZJUqVdQwrltuuUUdbI1kDowLLrhAvYZ8BxKwmKWd5GAgOy7ZYcnwt4YNG6r5B7wD98rQTr7aSG4yZ0Ogtzf5Tjp16qS2aznoGt+jorfTnj17VBCRnJys1gWZ80QOfMZ5LCpDO916661qe5Jll+1L9j96UFGR1yVe3ZSIiIgCpsLXWBAREVH4YGBBREREAcPAgoiIiAKGgQUREREFDAMLIiIiChgGFkRERBQwDCyIiIgoYBhYEBERUcAwsCAiIqKAYWBBRGVy880344orrgj1YhBRmGJgQURERAHDwIKIfJo8eTLat2+P2NhYVK9eHf369cOjjz6Kzz//HD/++CMsFou6zZ07Vz1/7969uPbaa1G1alUkJyfj8ssvx65du4pkOsaNG4caNWqoSzvfddddyMvLC+GnJKJAiwj4KxJRhZeWloYbbrgBL7/8MoYNG4bMzEz8+eefuOmmm7Bnzx5kZGTgs88+U8+VICI/Px8DBw5Ejx491PMiIiLwwgsvYNCgQVi7di2ioqLUc2fPno2YmBgVjEjQccstt6ig5V//+leIPzERBQoDCyLyGVjY7XZceeWVaNiwoXpMshdCMhi5ubmoVauW+/lfffUVHA4HPv74Y5XFEBJ4SPZCgogBAwaoxyTA+PTTTxEXF4e2bdviueeeU1mQ559/HlYrE6hEZsAtmYiK6NixI/r27auCiWuuuQYfffQRTpw4Uezz16xZg+3btyMhIQFVqlRRN8lk5OTkYMeOHR6vK0GFTjIcp06dUt0oRGQOzFgQURE2mw2zZs3CX3/9hZkzZ+Ktt97Ck08+iSVLlvh8vgQHXbp0wcSJE4v8TeopiKjyYGBBRD5Jl0bPnj3V7ZlnnlFdIlOnTlXdGQUFBR7P7dy5M7799lukpqaqosySMhunT59W3Sli8eLFKrtRv379cv88RBQc7AohoiIkM/Hiiy9i+fLlqlhzypQpOHLkCFq3bo1GjRqpgswtW7bg6NGjqnBzxIgRSElJUSNBpHhz586dqrbi/vvvx759+9yvKyNAbrvtNmzcuBHTp0/Hs88+i1GjRrG+gshEmLEgoiIk6zB//ny88cYbagSIZCtee+01DB48GF27dlVBg9xLF8gff/yB3r17q+ePHj1aFXzKKJK6deuqOg1jBkN+b968OXr16qUKQGXkydixY0P6WYkosCyapmkBfk0ioiJkHouTJ09i2rRpoV4UIipHzD8SERFRwDCwICIiooBhVwgREREFDDMWREREFDAMLIiIiChgGFgQERFRwDCwICIiooBhYEFEREQBw8CCiIiIAoaBBREREQUMAwsiIiIKGAYWREREhED5f5wLFdTaDR2wAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:     0.3481\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/4600 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "af414750f1eb4002b121a4aa6519fbed"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 56 / global_step 2576\n",
      "lr: 0.03\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:     0.3341\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/4600 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "63c2fa078cd94f3b8d82f3108cfe61f8"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 9 / global_step 414\n",
      "lr: 0.3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:     nan\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/4600 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "fc876fea3ae848be9a7026c3b1a5408a"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr: 0.001\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:     0.3710\n"
     ]
    }
   ],
   "execution_count": 12
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
